ResearchPublications

Understanding medical distrust among African American/Black and Latino persons living with HIV with sub-optimal engagement along the HIV care continuum: A machine learning approach
Abstract

Medical distrust is a potent barrier to participation in HIV care and medication use among African American/Black and Latino (AABL) persons living with HIV (PLWH). However, little is known about sociodemographic and risk factors associated with distrust. We recruited adult AABL PLWH from low socio-economic status backgrounds with insufficient engagement in HIV care (N = 512). Participants completed structured assessments on three types of distrust (of health care providers, health care systems, and counter-narratives), HIV history, and mental health. We used a type of machine learning called random forest to explore predictors of trust. On average, participants were 47 years old (SD = 11 years), diagnosed with HIV 18 years prior (SD = 9 years), and mainly male (64%) and African American/Black (69%). Depression and age were the most important predictors of trust. Among those with elevated depressive symptoms, younger participants had less trust than older, while among those without depression, trust was greater across all ages. The present study adds nuance to the literature on medical distrust among AABL PLWH and identifies junctures where interventions to build trust are needed most.

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Full citation:
He N, Cleland CM, Gwadz M, Sherpa D, Ritchie AS, Martinez BY, Collins LM (2021).
Understanding medical distrust among African American/Black and Latino persons living with HIV with sub-optimal engagement along the HIV care continuum: A machine learning approach
SAGE Open, 11 (4). doi: 10.1177/21582440211061314.